Assessment of empirical and physically-based approaches to simulate surface resistance for improved evapotranspiration modeling of winter wheat in semi-arid region, Morocco
Bibliographic record
Abstract
Evapotranspiration (ET) is a fundamental component of the water and energy balance, strongly influencing crop growth and productivity. Accurate ET estimation is critical in semi-arid regions, where water scarcity requires optimized management. Among the available approaches, the Penman–Monteith (PM) model is the most widely used for this purpose, its performance strongly depends on the accurate characterization of surface resistance ( r c ), a key parameter controlling ET estimations. In this study, two approaches for estimating r c were evaluated for winter wheat cultivated in the Haouz plain (Morocco) under contrasting irrigation regimes (full and deficit) during the 2016–2017 and 2017–2018 growing seasons. The first is a mechanistic formulation, based on the Jarvis model, which incorporates vapor pressure deficit ( VPD ) and soil water content ( θ ) to capture stomatal responses. The second is an empirical approach, using a thermal stress index ( SI ) derived from land surface temperature ( LST ), providing a rapid indicator of crop water status. Both approaches were integrated into the PM model and calibrated with eddy covariance data collected over a deficit-irrigated field in 2016/2017, then validated across both irrigation regimes and seasons. Results showed that the mechanistic approach reproduced ET dynamics under full irrigation (R² ≥ 0.73; RMSE < 0.6 mm·day⁻¹), but underestimated fluxes under severe stress. Conversely, the empirical approach, being more sensitive to short-term water status, outperformed under deficit irrigation (R² ≥ 0.79; RMSE < 0.6 mm·day⁻¹). Moreover, a critical SI threshold of 0.5 was identified, which could serve as a practical guideline for irrigation scheduling to reduce water losses. Overall, the results highlight the robustness and complementarity of both approaches and suggest the potential of hybrid models combining physiological realism with thermal sensitivity to improve irrigation management in water-limited areas. • Mechanistic and empirical approaches used to estimate canopy resistance. • The two approaches show complementarity across irrigation regimes. • Full irrigation, the mechanistic model performs best in reproducing ET (R²≥0.73). • Deficit irrigation, the empirical model performs best in reproducing ET (R²≥0.79). • SI 0.5 threshold provides a practical guideline for irrigation timing in wheat fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".